Andrés Corrada-Emmanuel’s Post

I remember learning category theory in undergrad and wondering: when would this ever be useful? Turns out it's very useful for program synthesis and software modeling -- thanks Kristopher for the great talk!

⚙️ "Category Theory for Legible Software and Corrigible World Modeling" by Kristopher Brown presented at FAR Labs Seminar. Brown discusses the role of category theory in creating interpretable and adaptable AI systems, aiming for enhanced safety and stakeholder alignment. 🌟 🔍 Model Legibility: Emphasizes the need for AI models that are both interpretable and economically viable. 🔧 Mathematical Principles: Advocates for software engineering based on principled mathematical models for abstraction reuse. 🔄 Stakeholder Collaboration: Highlights the importance of diverse stakeholder collaboration to align AI systems with broad human values. 🧩 Examples of Abstractions: Showcases how databases, Petri nets, and wiring diagrams exemplify effective abstraction in software engineering. 📺 Watch the full recording:  https://lnkd.in/ghT4irux  -- and subscribe to our YouTube channel for future research presentations!

Is causality necessary for trustworthy and explainable systems? From fair bandits to counterfactual explanations and back. In her doctoral thesis, Limor Gultchin presents a comprehensive overview of the field of trustworthy & explainable machine learning. The paper introduces a bunch of novel theoretical and practical contributions: a methodology for identifying mediation paths, feature-set-independent measure of necessity and sufficiency (I think this one's really exciting!) and more. IMHO, a must read for anyone interested in explainability, fairness, and trustworthy system design. #causality #causalinference #fairness #machinelearning #explainability #trustworthyAI 💡 Paper: https://vist.ly/vhbu

Back to writing. This title excites. Quote: Disruptors greatly bring discomfort to the order of the known. Unknowns are like an algebraic equation. They are meant to be solved in a stepwise process. X doesn’t just become an outcome without evidence of some path to solving its mystery. There must be that “that-makes-sense” moment to explain unknowns.

1ST PRINCIPLES THINKING... A first principle is a basic assumption that cannot be deduced any further. Over two thousand years ago, Aristotle defined a first principle as “the first basis from which a thing is known.” First principles thinking is a fancy way of saying “think like a scientist.” Scientists don’t assume anything. They start with questions like, What are we absolutely sure is true? What has been proven? In theory, first principles thinking requires you to dig deeper and deeper until you are left with only the foundational truths of a situation.

Mindsmith Thinking support by using our expertise to provide "insight' into interpretation. Developing interpretation is progress, and progress, is the path to better performance. Here we proved a practical cognitive assessment of politics and how opinion becomes truth: Politics is about messages based on information and opinion. Messages become narrative and influence. Influence becomes thinking and behaviour. The "political definition of truth", is better described by the use of he word narrative. Narrative, often supported by data, is convincing, but may not amount to truth. The progress we make is not confusing this definition as "truth" and seeing the use of data in this context. We remove "the veil, understand what we see, and act and direct our resources accordingly.

Mindsmith Thinking support by using our expertise to provide "insight' into interpretation. Developing interpretation is progress, and progress, is the path to better performance. Here we proved a practical cognitive assessment of politics and how opinion becomes truth: Politics is about messages based on information and opinion. Messages become narrative and influence. Influence becomes thinking and behaviour. The "political definition of truth", is better described by the use of he word narrative. Narrative, often supported by data, is convincing, but may not amount to truth. The progress we make is not confusing this definition as "truth" and seeing the use of data in this context. We remove "the veil, understand what we see, and act and direct our resources accordingly.

My inaugural Medium post will delve into a topic that I believe may capture your interest. If this subject piques your curiosity, I invite you to explore it.

As many of us know...more doesn't mean better. Reading ML papers, we are reminded of this every so often. In "Self-Alignment with Instruction Backtranslation" (which is a great paper btw), you will find the graph below ⬇ Spending time throwing away examples and curating smaller high quality datasets is not a wasted effort. Link to the paper: https://lnkd.in/eSnKEVxC

Call for Papers   https://lnkd.in/d79mf-8W    Special Session on Integrating Fuzzy and Hybrid Fuzzy Sets and Systems: A New Paradigm in Decision Making (RAMSA -2024) 🌟 Topics of interest include, but are not limited to: Theoretical foundations of fuzzy systems in decision making. Hybrid fuzzy systems combine fuzzy logic with other decision frameworks. Case studies on the impact of fuzzy systems on strategic decision-making. Development of hybrid models for predictive analysis in uncertain environments. Practical applications in various industries. Ethical considerations in decision making with fuzzy logic. 🔍 Seeking innovative approaches and insightful case studies!   Key Dates: 📅 Abstract Submission Deadline: Nov 30, '23 💳 Fee Payment & Author Registration: Dec 15, '23 📜 Full-Length Paper Submission: Dec 26, '23 🗓️ Conference Dates: Feb 29 - Mar 2, '24   Submit at: ssfuzzy570@gmail.com   How to Participate: 1. 📝 Prepare your abstract as per the template. 2. 🚀 Submit your abstract. 3. 📧 Await acceptance details and further steps. Paper publication: The specific publication venues, including journals, books, or proceedings, are currently under consideration and will be updated in due course. For current status please check updates. #CallForPapers #FuzzyLogic #DecisionMaking #Research #Innovation #Special Session RAMSA24